Since the advent of the first electronic computers in the 1940's, computers have continued to handle a greater variety of increasingly complex tasks. Advances in semiconductors and other hardware components have evolved to the point that current low-end desktop computers can now handle tasks that once required roomfuls of computers.
Computer programs, which are essentially the sets of instructions that control the operation of a computer to perform tasks, have also grown increasingly complex and powerful. While early computer programs were limited to performing only basic mathematical calculations, current computer programs handle complex tasks such as voice and image recognition, predictive analysis and forecasting, multimedia presentation, and other tasks that are too numerous to mention.
However, one common characteristic of many computer programs is that the programs are typically limited to performing tasks in response to specific commands issued by an operator or user. A user therefore must often know the specific controls, commands, etc. required to perform specific tasks. As computer programs become more complex and feature rich, users are called upon to learn and understand more and more about the programs to take advantage of the improved functionality.
In addition to being more powerful, computers have also become more interconnected through private networks such as local area networks and wide area networks, and through public networks such as the Internet. This enables computers and their users to interact and share information with one another on a global scale. However, the amount of information is increasing at an exponential rate, which makes it increasingly difficult for users to find specific information.
As a result of the dramatic increases in the both complexity of computer programs and the amount of information available to users, substantial interest has developed in the area of intelligent agent computer programs, also referred to as intelligent agents or simply agents, that operate much like software-implemented “assistants” to automate and simplify certain tasks in a way that hides their complexity from the user. With agents, a user may be able to perform tasks without having to know specific sequences of commands. Similarly, a user may be able to obtain information without having to know exactly how or where to search for the information.
Intelligent agents are characterized by the concept of delegation, where a user, or client, entrusts the agents to handle tasks with at least a certain degree of autonomy. Intelligent agents operate with varying degrees of constraints depending upon the amount of autonomy that is delegated to them by the user.
Intelligent agents may also have differing capabilities in terms of intelligence, mobility, agency, and user interface. Intelligence is generally the amount of reasoning and decision making that an agent possesses. This intelligence can be as simple as following a predefined set of rules, or as complex as learning and adapting based upon a user's objectives and the agent's available resources.
Mobility is the ability to be passed through a network and execute on different computer systems. That is, some agents may be designed to stay on one computer system and may never be passed to different machines, while other agents may be mobile in the sense that they are designed to be passed from computer to computer while performing tasks at different stops along the way. User interface defines how an agent interacts with a user, if at all.
Agents have a number of uses in a wide variety of applications, including systems and network management, mobile access and management, information access and management, collaboration, messaging, workflow and administrative management, and adaptive user interfaces. Another important use for agents is in electronic commerce, where an agent may be configured to seek out other parties such as other users, computer systems and agents, conduct negotiations on behalf of their client, and enter into commercial transactions.
Just as human agents have a certain amount of autonomy, intelligent agents similarly have a set of constraints on what they are authorized and not authorized to do. For example, a selling agent for electronic commerce applications may be constrained by a minimum acceptable price. However, a good selling agent, whether electronic or human, would never initially give its lowest acceptable price, as this would minimize profit margins. Furthermore, giving the lowest price may not even assure sales because a buyer may infer that the price is not competitive because the agent is unwilling to lower the price from the original offer. Therefore, an agent typically starts negotiations with some margin from its worst case acceptable price, then works toward a mutually acceptable price with the other party.
It is desirable for all agents, and particularly those in electronic commerce applications, to operate reliably, efficiently, and profitably on behalf of their clients. Any negotiation plans, techniques or strategies used by an intelligent agent to operate within its constraints, however, often should be hidden from other parties. Otherwise, the agent is placed at a competitive disadvantage. Given that many agents may be dispatched to unsecured environments, an assumption must be made that other parties may be able to scan or reverse engineer an agent to learn its negotiation strategy or other constraints. It must also be assumed that other parties may be able to decode messages sent between an agent and its client to obtain the greatest advantage in negotiation. The validity of such assumptions stems from the fact that these techniques are conceptually similar to many of the techniques used by some salespeople to obtain the best price possible.
If a selling agent uses a predictable algorithm to make offers, e.g., starting with a comfortable margin and halving the difference between the previous asked price and its lowest price with each new asked price, the other party may be able to detect this trend and predict the lowest price acceptable to the agent. Under these circumstances, the selling agent would rarely be able to negotiate a price higher than its minimum acceptable price.
Another desirable trait for intelligent agents is that of efficiency. In electronic commerce applications especially it is often desirable to maximize the number of trades at the best prices for the client. Any time that an intelligent agent spends in fruitless negotiations decreases the efficiency of the agent.
Furthermore, another concern with intelligent agents arises when the agents are interacting with unknown parties. For example, if agents interact with known, reliable agents, the relative risks to the agents may not be as great, and the agents may not be required to protect against adverse activities on the part of these parties. However, particularly in many unsecured environments, it is likely that the agents will interact with a number of unknown parties, which presents greater risks to the agents, and may require additional protections to be provided for the agent.
In addition, intelligent agents in electronic commerce applications must often be capable of determining a reasonable or acceptable value for a desired transaction. In many markets, especially those that are electronically controlled, market conditions can change rapidly. Stock, bond and commodity prices for example change continuously, and an agent which works with outdated information may enter into transactions that are well outside of the current market conditions at the time of the transactions. Moreover, some markets may be subject to manipulation by other parties attempting to obtain competitive advantages.
Therefore, a significant need exists in the art for an intelligent agent having productive, adaptive, secure and efficient negotiation skills for conducting commercial transactions on behalf of a client.